Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric,
radiometric and temporal resolution. In many applications the processing of such images needs high performance
computing techniques in order to deliver timely responses e.g. for rapid decisions or real-time actions. Thus, parallel or
distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical Processing Unit (GPU)
programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue
of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of
terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the
image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to
successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of
the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is
mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are
thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized
and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations.
Examples and comparisons with standard CPU processing are also reported and commented.